Passivity and passification of memristor-based recurrent neural networks with time-varying delays

Zhenyuan Guo, Jun Wang, Zheng Yan

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)

Abstract

This paper presents new theoretical results on the passivity and passification of a class of memristor-based recurrent neural networks (MRNNs) with time-varying delays. The casual assumptions on the boundedness and Lipschitz continuity of neuronal activation functions are relaxed. By constructing appropriate Lyapunov-Krasovskii functionals and using the characteristic function technique, passivity conditions are cast in the form of linear matrix inequalities (LMIs), which can be checked numerically using an LMI toolbox. Based on these conditions, two procedures for designing passification controllers are proposed, which guarantee that MRNNs with time-varying delays are passive. Finally, two illustrative examples are presented to show the characteristics of the main results in detail.

Original languageEnglish
Article number6774460
Pages (from-to)2099-2109
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number11
DOIs
Publication statusPublished - 1 Nov 2014
Externally publishedYes

Keywords

  • Linear matrix inequality (LMI)
  • memristor
  • passification
  • passivity
  • recurrent neural network.

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